In many medical disciplines, diagnosis and follow-up of diseases relies to an ever increasing degree on the interpretation of data obtained by medical imaging techniques. In the field of neurology, magnetic resonance (MR) imaging has become particularly important, as it has specific advantages over X-ray based computerized tomography (CT) in certain situations.
In particular, the (estimated) volume of white matter lesions is used as a biomarker for some neurological diseases, in particular multiple sclerosis (MS). In past years, it has been attempted to automate the detection of such lesions on the basis of MR images.
In the article Automated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detection (IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 20, NO. 8, Aug. 2001), K. VAN LEEMPUT et al. present an algorithm for segmentation of multiple sclerosis lesions from multispectral magnetic resonance images. In the method described in that article, MS lesions are detected as outliers with respect to a statistical model for normal brain tissue intensities in MR images.
In the article An Automatic Segmentation of T2-FLAIR Multiple Sclerosis Lesions (The MIDAS Journal—MS Lesion Segmentation (MICCAI 2008 Workshop)), JC. SOUPLET et al. present a method designed to detect a hyperintense signal area on a T2-FLAIR sequence. The disclosed algorithm uses three conventional MRI sequences: T1, T2 and T2-FLAIR. First, images are cropped, spatially unbiased and skull-stripped. A segmentation of the brain into its different compartments is performed on the T1 and the T2 sequences. From these segmentations, a threshold for the T2-FLAIR sequence is automatically computed. Then post-processing operations select the most plausible lesions in the obtained hyperintense signals.
In the article Evaluating and Reducing the Impact of White Matter Lesions on Brain Volume Measurements (Human Brain Mapping Volume 33, Issue 9, 2011), M. BATTAGLINI et al. describe how the presence of white matter lesions affects certain segmentation-based brain volume measurements. The article indicates that refilling the lesions with intensities matching the surrounding normal-appearing white matter ensured accurate tissue-class measurements and thus represents a promising approach for accurate tissue classification and brain volume measurements.
There is a need for an automated way to more accurately and reliably estimate the respective volumes of white matter, gray matter, and/or cerebrospinal fluid (CSF), in the presence of lesions, in particular white matter lesions.